Our proposed sampling for the Randomized Newton Method is based
on a class of distributions called Determinantal Point Processes
(DPPs). Originally proposed by [1]}, DPPs have found numerous
applications in machine learning [2]} as well as optimization
[3]}, [4]}, for their variance reduction properties and the ability to produce diverse samples.

For a \(d\times d\)  p.s.d. matrix \(\) , we define \(()\)  as a distribution over all subsets \(S\subseteq [d]\) , so
that
\((S)\propto \det (_{SS}).\) 
